Sign up to receive free email alerts when patent applications with chosen keywords are publishedSIGN UP

Abstract:

Tax returns are received from one or more tax agencies. Each tax return
is compared to a stored profile, and a determination is made as to
whether each tax return falls within a trend. An evaluation of the tax
return is generated based on the comparison and the determination of
whether the tax return falls within the trend. The evaluation includes an
indication of the tax returns potential to be a fraudulent tax return.

Claims:

1-20. (canceled)

21. A computer readable storage medium including computer code that when
executed by a processor performs a method of evaluating a tax return, the
method comprising: receiving a plurality of data fields from a tax
return; comparing values for at least some of the plurality of data
fields from the tax return with values for factors in a trend profile
corresponding to the at least some of the plurality of data fields;
determining whether the tax return falls within a trend from the
comparing; in response to determining the tax return falls within the
trend, using a trend score modifier to determine a score for the tax
return; and generating an evaluation of the tax return based on the
score.

22. The computer readable storage medium of claim 21, wherein the method
comprises: in response to determining the tax return does not fall within
the trend, determining the score for the tax return without using the
trend score modifier.

23. The computer readable storage medium of claim 21, wherein each of the
factors in the trend profile identifies a data field in the tax return
that is considered relevant to an objective of the evaluation.

24. The computer readable storage medium of claim 21, wherein the
objective is to identify fraudulent tax returns or misreportings in tax
returns.

25. The computer readable storage medium of claim 21, wherein the method
comprises: determining the tax return is fraudulent or misreported if the
tax return is determined to fall within the trend.

26. The computer readable storage medium of claim 21, wherein the method
comprises: determining whether values in data fields in the tax return
match information from one or more data sources other than a tax agency,
wherein the information is indicative of a fraudulent tax return.

27. The computer readable storage medium of claim 21, wherein comparing
values for at least some of the plurality of data fields from the tax
return with values for factors in a trend profile corresponding to the at
least some of the plurality of data fields comprises: identifying the at
least some of the plurality of data fields from the plurality of data
fields from the tax return that correspond to the factors in the trend
profile; and determining a score for each of the corresponding data
fields in the tax return based on the comparisons of the values for the
factors in the trend profile to the values in the corresponding data
fields in the tax return, wherein each score represents a weighting of
the factor for the corresponding data field, wherein the score for the
tax return is determined from the scores for the corresponding data
fields.

28. The computer readable storage medium of claim 21, wherein determining
whether the tax return falls within a trend comprises: determining
whether the values for the at least some of the plurality of data fields
from the tax return match the values for factors in the trend profile
corresponding to the at least some of the plurality of data fields; in
response to determining the values for the at least some of the plurality
of data fields match the values for the corresponding factors,
determining the tax return falls within the trend.

29. The computer readable storage medium of claim 21, wherein the method
comprises reporting the score to a tax agency.

30. The computer readable storage medium of claim 21, wherein determining
whether the tax return falls within a trend comprises: determining if the
values for a majority of the factors match the values for the
corresponding data fields in the tax return that correspond with the
majority of the factors.

31. The computer readable storage medium of claim 21, wherein determining
whether the tax return falls within a trend comprises: determining if the
values for all the factors match the values for the corresponding data
fields in the tax return.

32. The computer readable storage medium of claim 21, comprising:
determining the trend profile by identifying factors from tax returns
determined to highly likely be fraudulent; and storing the trend profile,
wherein the trend profile includes the factors from tax returns
determined to highly likely be fraudulent.

33. A tax return evaluation system comprising: a database storing a trend
profile from a tax agency, the trend profile including factors for
evaluating tax returns; a computer system to receive a plurality of data
fields from a tax return, compare values for at least some of the
plurality of data fields from the tax return with values for the factors
in the stored trend profile corresponding to the at least some of the
plurality of data fields, determine whether the tax return falls within a
trend from the comparing; in response to determining the tax return falls
within the trend, using a trend score modifier to determine a score for
the tax return; and generate an evaluation of the tax return based on the
score.

34. The tax return evaluation system of claim 33, wherein the computer
system, in response to determining the tax return does not fall within
the trend, determines the score for the tax return without using the
trend score modifier

35. The tax return evaluation system of claim 33, wherein the computer
system determining a tax return falls within the trend comprises:
determining if the values for the data fields match the values for the
factors in the stored trend profile; in response to determining the
values match, the computer system determines the tax return falls within
the trend.

36. The tax return evaluation system of claim 33, wherein the evaluation
is indicative of a likelihood of fraud for each tax return.

37. The tax return evaluation system of claim 33, wherein the score is
calculated by determining a score for each factor in the stored trend
profile based on a value for the corresponding data field and each score
represents a weighting of the factor for the corresponding data field,
and combining the scores to determine the score for the tax return.

38. The tax return evaluation system of claim 33, wherein the computer
system comprises a web server configured to receive tax returns from the
tax agency via the Internet and report the evaluation to the tax agency
sending the tax return.

39. The tax return evaluation system of claim 33, wherein the computer
system is connected to a plurality of data sources, and the data sources
provide information for determining the evaluation of each tax return.

40. A method of evaluating tax returns comprising: receiving a plurality
of data fields from a tax return; comparing values for at least some of
the plurality of data fields from the tax return with values for factors
in a trend profile corresponding to the at least some of the plurality of
data fields; determining whether the tax return falls within a trend from
the comparing; in response to determining the tax return falls within the
trend, using a trend score modifier to determine a score for the tax
return; and generating, by a computer system, an evaluation of the tax
return based on the score.

Description:

BACKGROUND

[0001] Tax returns are filed from several sources, including through the a
federal or state electronic filing program, directly from taxpayers via
tax preparation software or paper-based returns, or bulk-filing from tax
preparers and data-entry vendors. The government tax agency handling the
filings, such as the Internal Revenue Service (IRS) or state revenue
departments, typically enters the information from the tax returns for
each filer into their internal database, and then human auditors may
review the data to identify fraudulent or inaccurate returns.

[0002] The conventional auditing process, however, provides little
collaboration or validation from other tax agencies and data sources, and
as a result is less likely to capture multi-state tax fraud campaigns.
For example, a person may attempt to defraud multiple states by filing
tax returns in ten different states under a social security number for a
deceased person. Self-contained systems of each state agency are not able
to identify that refunds are being requested under the same social
security number for multiple different states, and may be unable to
connect with any databases that store information about the deceased.
Furthermore, this type of fraud may not be captured by the traditional
auditing process, if none of the flags are triggered. For example, if the
fraudulent returns are each claiming a modest refund, e.g., $1500.00 or
less, the returns may fall below a threshold that triggers a flag for an
audit, and the refunds may be paid.

BRIEF DESCRIPTION OF DRAWINGS

[0003] The embodiments of the invention will be described in detail in the
following description with reference to the following figures.

[0004]FIG. 1 illustrates a tax return evaluation system, according to an
embodiment;

[0005]FIG. 2 illustrates a method for evaluating tax returns, according
to an embodiment;

[0006] FIG. 3 illustrates a method for determining a trend profile,
according to an embodiment; and

[0007] FIG. 4 illustrates a computer system that may be used for the
methods and system, according to an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

[0008] For simplicity and illustrative purposes, the principles of the
embodiments are described by referring mainly to examples thereof. In the
following description, numerous specific details are set forth in order
to provide a thorough understanding of the embodiments. It will be
apparent however, to one of ordinary skill in the art, that the
embodiments may be practiced without limitation to these specific
details. In some instances, well known methods and structures have not
been described in detail so as not to unnecessarily obscure the
embodiments.

1 Overview

[0009] According to an embodiment, a tax return evaluation system
identifies potential taxpayer fraud by examining data fields in filed
taxpayer returns. Examples of the data fields include social security
number, address, refund requested, withholding, employer ID, tax prepare
ID, bank account information, adjusted gross income (AGI), etc. The data
fields or information derived from the data fields (e.g., ratio of refund
to AGI) are compared to profiles and information from multiple data
sources to identify potentially fraudulent returns. The profiles are
custom in that they can be created and provided by tax agencies or other
entities requesting evaluation by the tax return evaluation system. For
their profiles, each tax agency can pick the data fields and values for
the data fields, which may be thresholds, that are considered flags for
detecting fraudulent returns. The data fields in the profiles are
referred to as factors. Furthermore, an agency may have more than one
profile for detecting different types of fraud or incorrect returns.

[0010] In one embodiment, the tax return evaluation system is a central
system that is connected to the data sources and is configured to receive
and store the profiles from each tax agency. The data sources may include
public databases or other public data sources, collaborating government
agencies, or an internal data source compiling information from tax
returns from multiple tax agencies. Examples of the data in the data
sources include an invalid social security number database, deceased
information database, criminal warrants and liens database, property
assessments database, and known fraudulent tax preparers and filers. The
tax return evaluation system receives the tax returns from the tax
agencies and uses the custom profiles and information from the data
sources for fraud detection. The tax return evaluation system may use a
service-oriented architecture, and may be provided in the form of a
web-based application supported by a relational database management
system. In other embodiments, some or all of the functionality of the tax
return evaluation system can be provided as part of a system for a
particular tax agency, such as incorporated with the IRS's current tax
system or incorporated in a state tax system. The tax agency may be a
government agency responsible for collecting taxes.

[0011] According to an embodiment, the tax return evaluation system
performs a trend analysis to identify factors that are associated with
potentially fraudulent returns. The trend analysis may encompass an
intra-trend analysis that analyzes factors within a single return and an
inter-trend analysis that analyzes factors across multiple tax returns,
which may include returns from multiple states and the IRS.

[0012] Also, a scoring function may be applied to score each tax return
based on a comparison to one or more profiles, which may be the custom
profiles provided by the tax agency or trend analysis profiles. A score
is generated for each return and is used to determine whether the return
is fraudulent. Also, the trend analysis can impact the score if a
determination is made that the return falls within a trend of potentially
fraudulent returns.

2. System Diagram

[0013]FIG. 1 illustrates a tax return evaluation system 100, according to
an embodiment. The tax return evaluation system 100 includes a tax
computer 110, a profiles database 111, and a tax return database 112. The
tax evaluation system 100 may include other well known components. The
tax computer 110 includes one or more computer systems for evaluating tax
returns. The profiles database 111 stores profiles, which may be received
from tax agencies 101a-n or other entities. The tax return database 112
stores received tax returns to be evaluated. The tax agencies 101a-n may
include state tax agencies, and/or the IRS. The tax return evaluation
system 100 is also connected to data sources 102a-f. These are data
sources that provide information that can be used for evaluating tax
returns for fraud. The data sources 102a-f may be publically available
data sources, private data sources or government data sources.

[0014] The tax return evaluation system 100 receives tax returns from the
tax agencies 101a-n. For example, the tax agencies 101a-n collect the tax
returns from their tax payers and send the returns to the tax return
evaluation system 100.

[0015] The tax returns may be sent in batch jobs or in real-time, as they
are received by the tax agencies 101a-n. The tax return database 112
stores the received tax returns.

[0016] The tax return evaluation system 100 evaluates each tax return for
fraud using the stored profiles in the profile database 111. The stored
profiles include factors for evaluating tax returns. The factors are
associated with data fields in a tax return. Examples of the factors and
data fields are social security number, tax payer ID, address, any
banking information, tax preparer ID/name/address, refund amount, AGI,
withholding amount; whether the filer is a first time filer, whether the
address is out-of-state and from a non-contiguous state, whether refunds
were previously requested by the filer and how much, etc.

[0017] The factors have associated values in the profile. The values may
be values for the factors. For example, a profile may want to evaluate
non-contiguous, out-of-state filers that request refunds between $1500.00
and $3000.00. The range between $1500.00 and $3000.00 are values for the
refund amount factor. Non-contiguous and out-of-state are values for a
factor consisting of address of the filer.

[0018] The tax computer 110 uses a profile for a tax agency to identify
fraud for tax returns from the jurisdiction of the agency. The profile
may be retrieved from the profiles database 111. For example, values from
the data fields in each tax return are extracted. These values are
compared to values in the profile, such as the range between $1500.00 and
$3000.00 for refund amount, and non-contiguous and out-of-state for
address. If values from a tax return match the values in the profile,
then an evaluation of the return for fraud is generated. An example of a
match is if the tax return includes a refund amount data field value of
$2000.00, because it is in the range between $1500.00 and $3000.00
specified for the refund amount factor in the profile. A data field from
a tax return that is compared to a factor in a profile for matching is
referred to as a corresponding data field for the factor. Also, if a
value of a corresponding data field satisfies the value of its factor,
such as the example where the data field value falls within the range for
the factor, the factor and corresponding data field are described as
matching.

[0019] An evaluation is generated for the return that indicates the
likelihood or probability that the return is fraudulent. The evaluation
varies depending on multiple criteria, which may include number of
matches, the type of matching factors, trends, and others. Also, values
from the tax return are compared to information from the data sources
102a-f. Matches between the values from the tax return and the
information from the data sources 102a-f impact the evaluation of the tax
return. For example, if the data source 102a includes social security
numbers for deceased individuals, and the social security number from the
tax return matches a social security number from the data source 102a,
then the tax return may be marked as fraudulent. In another example, the
data source 102b includes information for people previously convicted for
fraud. A match between the data source 102b and a tax return impacts the
evaluation to indicate a greater likelihood of fraud.

[0020] Trends are also detected using the profiles. In one example, a
trend is identified if multiple values for factors in the profile match
values in data fields in a tax return. If a trend is detected, the trend
impacts the evaluation of the tax return, for example, by indicating an
increased likelihood of fraud. Other types of trends that are associated
with factors across multiple tax returns may also be detected, and trend
profiles are created for these types of trends.

[0021] As indicated above, the evaluation of a tax return indicates a
probability or likelihood of fraud. The evaluation is not necessarily a
"yes" or "no" answer of whether a tax return is fraudulent, and may
indicate the degree of likelihood of fraud. In one embodiment, scoring is
used to determine the evaluation of the tax return and to indicate the
degree of likelihood of fraud. A score is generated by the tax return
evaluation system that indicates the likelihood the tax return is fraud.
In one example, the score is between 1 and 100, where 100 is the highest
likelihood of fraud. Other scoring ranges may alternatively be used. A
trend multiplier is used to change the score if a trend is detected.
Other criteria also impact the score. Examples of the scoring are
described in further detail below.

[0022] As described above, the tax return evaluation system 100 uses
profiles to evaluate tax returns for fraud. However, the tax return
evaluation system 100 maybe used to evaluate tax returns to achieve other
objectives. For example, fraud typically includes intentional
misrepresentation in the tax return. The tax return evaluation system may
be used to identify unintentional misreporting in tax returns using
profiles. Furthermore, the profiles may be used to identify tax returns
for auditing. For example, if a tax return matches multiple factors in a
profile, the tax return is flagged for further auditing by the tax
agency.

[0023] In one embodiment, the tax return evaluation system 100 is a
service that is accessed via the Internet. For example, each of the tax
agencies 101a-n uploads tax returns to the tax return evaluation system
100 via a web interface, and the tax returns are stored in the tax return
database 112. The tax return evaluation system 100 evaluates each return
and sends the evaluations to the tax agencies 101a-n or makes the
evaluations available to the tax agencies 101a-n for downloading via the
web interface. In this embodiment, the tax return evaluation system
operates as a central, remote system that is accessible via the Internet
or other private or public networks. Also, the tax return evaluation
system 100 is able to capture information from tax agencies, which can be
used to identify trends across multiple jurisdictions.

[0024] Because of data sensitivity issues, the tax agencies 101a-n may not
send entire tax returns. For example, instead of receiving and storing
entire tax returns, the tax return evaluation system 100 may only receive
predetermined line items from each return, and store those line items in
the tax return database 112. These line items are then compared to one or
more stored profiles to evaluate each return.

[0025] In another embodiment, the tax return evaluation system 100 is
incorporated in a local tax computer system of the tax agency. In this
embodiment, the tax return evaluation system 100 may not have the benefit
of accessing tax return data from other jurisdictions. However, in this
embodiment, the system may be more secure in that data from tax returns
that is sent to the tax return evaluation system 100 remains internal to
the same system that receives the tax returns from the tax payers, and is
not provided or stored with tax payer information from other
jurisdictions. However, data storage policies may be instituted for the
central tax return evaluation system embodiment to maintain
confidentiality and to keep data from different jurisdictions separated
as needed. Furthermore, in the embodiment where the tax return evaluation
system 100 operates as a central, remote system, secure communication
between the tax return evaluation system 100 and the tax agencies 101a-n
and the data sources 101a-f as needed may be provided through
conventional techniques, such as Secure Sockets Layer (SSL).

3. Scoring Examples

[0026] The tax return evaluation system 100 is operable to generate an
evaluation for each tax return that indicates a likelihood of fraud. In
one embodiment, scoring is used to generate the evaluation. Scoring may
be based on multiple criteria, such as the number of matches found
between data fields (e.g., line items) in the tax return and factors in a
profile, matches between the data fields and information from the data
sources 102a-f, the types of matches identified, where type is associated
with the type of factor or data source that has a match, and weights for
the types of matches. The evaluation of a tax return may be a report
including a score and all the pertinent matching information.

[0027] In an example, suppose the tax agency 101a wants to identify fraud
schemes from out-of-state filers. The tax agency 101a sends a profile 200
to the tax return evaluation system 100. The profile 200 includes the
following factors and associated values shown in table 1.

[0028] The profile 200 has three factors including address of the tax
payer, refund amount, and tax preparer. The values for each factor are
also shown. The profile 200 may also specify weights for each value of a
factor, which is used to calculate a score. For example, for address, a
score of 10 is given if the address in a tax return is out-of-state but
is in a contiguous state. If the address is both out-of-state but and in
a non-contiguous state, the profile may specify a higher weight. For
example, a score of 30 is given if both values are matched. If the refund
amount in the tax return falls within the range of $1,500.00-$3,000.00,
then a score of 25 is given for that factor based on the assigned
weighting for that factor. As multiple tax returns are evaluated, the tax
return evaluation system 100 is able to identify tax preparers filing tax
returns that match the address and refund amount factors. If the tax
return being evaluated has one of these tax preparers (e.g., ABC tax
preparers) as its tax preparer then a score of 35 is given. The scores
for each factor are accumulated to determine a final score for the tax
return. The final score along with all the pertinent matching information
is reported to the tax agency 101a.

[0029] The final score is also changed based on whether a trend is
identified when evaluating a return. The trend multiplier is an amount
that is multiplied by a score to account for an identified trend. The
profile may specify the trend multiplier. The trend multiplier may be
applied to the accumulated score to determine the final score. For
example, a trend is identified for a tax return if multiple data fields
match the factors in the profile. If the tax return has an address that
is out-of-state and has a refund amount of $1,500.00 then the tax return
has multiple matches. If the trend multiplier is 1.3, then 1.3 is
multiplied by the accumulated score of 35 to determine the final score of
45.5. In other examples, the trend multiplier may be applied only to the
scores that are for the matching factors. Also, the trend may be defined
as multiple matching factors, or a majority of the factors are matched by
the data fields in the tax return, or all the factors in the profile are
matched by the data fields in the tax return.

[0030] Other criteria may influence the score. Information from the data
sources 102a-f that matches data fields in the tax return may cause the
score to be increased. For example, if the data source 102a includes
social security numbers for deceased individuals, and the social security
number from the tax return matches a social security number from the data
source 102a, then the tax return may be given a maximum score. In another
example, the tax return evaluation system 100 may compile information for
fraudulent returns and use the information to generate profiles. Scores
for tax returns matching these profiles are increased.

[0031] The profiles may be updated based to detect new fraud schemes and
other incorrect tax returns. Thus, the tax return evaluation system 100
allows for dynamic evaluation of tax returns that allows the factors and
weighting of the factors to be modified as needed.

4. Flowcharts

[0032]FIG. 2 illustrates a flowchart 200 for evaluating a tax return,
according to an embodiment. The methods described herein may be described
with respect to the tax return evaluation system 100 shown in FIG. 1 by
way of example and not limitation. The methods may be practiced in other
systems. Also, some of the steps of the methods may be performed in
different orders than shown.

[0033] At step 201, profiles are received from the tax agencies 101a-n and
stored in the profiles database 111.

[0034] At step 202, a tax return is received from a tax agency, such as
the tax agency 101a. Instead of receiving an entire tax return, a
plurality of line items (i.e., data fields) from the tax return is
received. The tax return may be provided in batch job with several other
returns or in real time as it is received by the tax agency.

[0035] At step 203, a profile for the tax agency 101a is identified. For
example, a profile for the tax agency 101a is retrieved from the profiles
database 111. A tax agency may have multiple profiles. In that case, the
steps for evaluating the profile against the return is performed for each
of the profiles.

[0036] At step 204, the tax return evaluation system 100 compares the tax
return to the profile. The tax return evaluation system 100 determines
whether any of the received data fields in the tax return match the
factors in the profile. If no, then the tax return is marked as proper or
not fraudulent at step 205. If yes, then, at step 206, an evaluation of
the tax return is generated that includes an estimation of the likelihood
the tax return is fraudulent and is based on the matching factors. This
estimation may be an intermediate evaluation that is modified before a
final evaluation is determined. For example, a score is determined for
each matching factor. Also, weightings for the factors may be used to
determine the scores. The scores for the factors are intermediate
evaluations for the tax return.

[0037] At step 207, the tax return evaluation system 100 determines
whether the tax return falls within a trend based on the comparison of
the tax return to the factors in the profile. The trend may be defined as
multiple factors in the profile matching data fields in the tax return,
or a majority of the factors matching data fields in the tax return, or
all the factors matching data fields in the tax return. The tax return is
determined to fall within the trend if multiple factors in the profile
match data fields in the tax return, or if a majority of the profiles are
matched, or if all the profiles are matched, depending on how the trend
is defined.

[0038] At step 208, if the tax return falls within the trend, then one or
more evaluations determined at step 206 are modified to take into
consideration the trend. For example, a trend multiplier is used to
modify an accumulated score calculated from the scores for each matching
factor.

[0039] At step 209, the tax return evaluation system 100 determines
whether information from one or more of the data sources 102a-f matches
the tax return. If yes, then, at step 210, the evaluation of the tax
return, such as determined by the previous steps, is modified to take
into consideration the matching. This may include increasing the score by
a predetermined amount or a predetermined multiple.

[0040] At step 211, the tax return evaluation system 100 determines
whether the tax return falls within a trend profile. For example, the tax
return evaluation system 100 may generate trend profiles based on
information from previous returns that were considered highly likely to
be fraudulent. The tax return evaluation system 100 compiles the factors
that are common to those profiles to generate a trend profile. If the tax
return falls within the trend profile, then, at step 212, the evaluation
of the tax return, such as determined by the previous steps, is modified
to take into consideration the trend. This may include using a trend
multiplier or other predetermined value to modify the score.

[0041] At step 213, a final evaluation is determined. This may include a
score determined based on the previous steps. At step 214, the final
evaluation is reported to the tax agency 101a, and may include the final
score and any pertinent matching information.

[0042] The steps of the method 200 are repeated to evaluate each return.
It should be noted that in some embodiments, some of the steps may be
optional. For example, a tax return may not be evaluated against a trend
profile at step 211, or if information is not available from the data
sources 102a-f, then step 209 is not performed.

[0043] FIG. 3 illustrates a method 300 for determining a trend profile,
according to an embodiment. As described with respect to step 211, the
tax return evaluation system 100 determines whether the tax return falls
within a trend profile. The tax return evaluation system 100 may generate
the trend profile based on information from previous returns that were
considered highly likely to be fraudulent.

[0044] At step 301, the tax return evaluation system 100 identifies tax
returns highly likely to be fraudulent. This may include tax returns
having a score greater than a threshold. Tax returns from multiple tax
agencies may be identified.

[0045] At step 302, common data fields from the tax returns identified at
step 301 are determined. At step 303, a trend profile is determined from
the common data fields. For example, the tax return evaluation system 100
determines that many tax returns from non-contiguous, out-of-state filers
having the same tax preparer are highly likely to be fraudulent. The data
fields of address and tax preparer and the values for the fields
comprising non-contiguous, out-of-state filers and the name of the tax
preparer that is the same for the tax returns become the factors and
corresponding values for the factors in the trend profile.

[0046] At step 304, the trend profile is stored in the profiles database
111. A trend profile may be applicable for particular returns. For
example, a trend profile may be compiled from tax returns from a
particular region, such as states in the northeast, and the trend profile
is only applied to tax returns from that region.

5. Computer Diagram and Computer Readable Medium

[0047] FIG. 4 shows a computer system 400 that may be used with the
embodiments described herein. The computer system 400 represents a
generic platform that includes components that may be in a server or
other computer system. The computer system 400 may be used as a platform
for the tax return evaluation system 100 and the tax computer 110 shown
in FIG. 1, and represents a computer system configured to execute one or
more of the methods, functions and other steps described herein. These
steps may be embodied as software stored on one or more computer readable
mediums.

[0048] The tax return evaluation system 100 may also be provided as an
enterprise system executed on multiple computer systems, such as multiple
servers. For example, if the tax return evaluation system 100 may include
an application server and a database server. Also, the tax return
evaluation system 100 may include a web server handling requests from the
tax agencies 101a-n to evaluate tax returns.

[0049] The computer system 400 includes one or more processors 402 that
may implement or execute software instructions performing some or all of
the methods, functions and other steps described herein. Commands and
data from the processor 402 are communicated over a communication bus
404. The computer system 400 also includes a main memory 406, such as a
random access memory (RAM), where the software and data for processor 402
may reside during runtime, and a secondary data storage 408, which may be
non-volatile and stores software and data. The memory and secondary data
storage are examples of computer readable mediums.

[0050] The computer system 400 may include one or more I/O devices 410,
such as a keyboard, a mouse, a display, etc. The computer system 400 may
include a network interface 412 for connecting to a network. It will be
apparent to one of ordinary skill in the art that other known electronic
components may be added or substituted in the computer system 400.

[0051] One or more of the steps of the methods described herein and other
steps described herein and one or more of the components of the systems
described herein may be implemented as computer code stored on a computer
readable medium, such as the memory and/or secondary storage, and
executed on a computer system, for example, by a processor,
application-specific integrated circuit (ASIC), or other controller. The
code may exist as software program(s) comprised of program instructions
in source code, object code, executable code or other formats. Examples
of computer readable medium include conventional computer system RAM
(random access memory), ROM (read only memory), EPROM (erasable,
programmable ROM), EEPROM (electrically erasable, programmable ROM), hard
drives, and flash memory.

[0052] While the embodiments have been described with reference to
examples, those skilled in the art will be able to make various
modifications to the described embodiments without departing from the
scope of the claimed embodiments. Furthermore, the embodiments described
herein may be used in combination with each other.